The lifetime of underwater sensor networks (USNs) can be prolonged significantly thanks to wireless power transfer technology. In this paper, we first proposed a Shortest Path Partial Charging based on Charging Curve Scheme (SPBS) to increase the survival rate of nodes in 3D underwater networks, and then we proposed a concept of secondary charging stations for mobile charging ships to reduce the traveling cost and improve charging efficiency. We first use k-means clustering algorithm to divide our network with k clusters, and then we place our secondary stations at k clustering centers, in this way, mobile charging ships can be charged at secondary stations quickly. Based on secondary stations, we proposed Hamilton Charging Scheme (HCS) using the Hamilton ring, and then we proposed a temporal and spatial collaborative charging algorithm (mCS-TS) for USNs with multiple mobile charging ships and secondary charging stations, which also takes the cluster factor and deadline time into consideration. Simulation results show the effectiveness of our proposed algorithms.

Introduction

With the development of emerging information and communication technologies, such as intelligent Internet of things, 5G, cloud computing, artificial intelligence and machine learning, great changes have taken place in people’s lifestyles. In traditional networks, sensor nodes are powered by batteries with limited capacity, which limits the working hours of sensor nodes. Battery exhaustion means the end of the sensor node lifetime. Therefore, the battery capacity becomes a main factor that restricts the lifetime of the whole sensor network. Fortunately, the advancement of wireless communication and microelectronic technology furthermore contributed to the emergence of wireless sensor networks and bring a new choice for extending batteries lifetime. Wireless Sensor Networks (WSNs) are composed of a large number of sensors that are deployed in the monitoring area in a self-organizing and multi-hop manner. Characterized by low cost, low power consumption and multiple functions [1]. WSNs are widely used in important fields such as environmental detection, military, smart home and telemedicine [2], [3]. In remote areas or areas where human intervention is not suitable, replacing the battery for the sensor nodes are troublesome and costly. Therefore, it is necessary to extend the lifetime of WSNs, and the energy consumption of sensor nodes in the area must be equalized and then study the energy supply technology of sensor nodes.